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MIT Graduate Student Develops Method To Quickly Model Complex Behavior

An MIT graduate student has developed a methodology for automatically constructing computer models that can accurately describe the behavior of complex systems with very little background information. The work has numerous potential applications, from enabling oil companies to get a clearer picture of where oil might be located underground to allowing port operators to spot suspicious behaviors.

Graduate student Emily Fox, of MIT's Laboratory for Information and Decision Systems, developed the methodology to build models for complicated systems whose behavior is characterized by abrupt changes. These complex dynamic systems include stock markets and dancing bees. Honeybees switch between several dances in seemingly random fashion, and stock markets are notoriously unpredictable.

While many researchers in diverse fields have focused on the modeling of dynamic systems, most require constraining assumptions such as a single, consistent mode of dynamic behavior and possibly prior information regarding the structure of the underlying dynamics.

"It's quite exciting that even when you remove the shackles of putting in prior information, there's a lot you can discover about a complex system," said Alan Willsky, Fox's advisor and professor of electrical engineering.

The new methodology sifts through sets of data, looks for patterns, and comes up with equations that describe these patterns.

In the case of the honeybee, Fox told the model the position of the bee and its head angle for 30 seconds, taking data in each of 30 frames per second. From that information, the model came up with the number of different dances, the bee's dancing state at each time point, the probability that the bee would switch to a different dance at each point, and equations that described each dance.

The methodology, which aims to come up with the simplest model that explains the data, accurately concluded that the honeybees have three dances. Honeybees use the dances to communicate distance and direction of potential food sources or nest sites. Previous efforts to study the creatures required painstaking review of long videos and visually categorizing the dances.

Fox also tested the model on data from the Brazilian stock market, using the same algorithm she used for the dancing bees. Given information on the Brazilian market's daily returns over a four-year period, the model inferred the number of modes of market volatility and the probability that the market would shift to a different state of volatility.

While the primary objective of this research is discovering models that can explain complex behavior, the extracted models could also be used as the basis for real-time estimation, tracking, and prediction.

The research was funded by the U.S. Army Research Office and the Air Force Office of Scientific Research.

About the Author

Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.

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